International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 3 Issue 5, August 2019 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
Determinants of Foreign Direct Investment in Nigeria
Dibua, Emmanuel Chijioke (Ph.D)1; Edoko, Tonna David (Ph.D)1;
Onwuteaka, Ifeoma Cecilia (Ph.D)2
1Department
2Departent
of Business Administration, Nnamdi Azikiwe University (NAU), Awka, Nigeria
of Economics, Chukwuemeka Odumegwu Ojukwu University, Igbariam Campus, Nigeria
How to cite this paper: Dibua, Emmanuel
Chijioke | Edoko, Tonna David |
Onwuteaka, Ifeoma Cecilia "Determinants
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ABSTRACT
Extant literature is replete with the benefit of attracting Foreign Direct
Investment (FDI) into an economy, it not only provides developing countries
with the much needed capital for investment; it also enhances job creation,
managerial skills as well as transfer of technology. However, attracting and
sustaining FDI inflow in Nigeria have remained a teething problem. This study
therefore examined the determinants of foreign direct investment in Nigeria.
Specifically the study provides empirical evidence on the influence trade
openness, market size, infrastructure, human capital, labour force, natural
resources, exchange rate and inflation rate on Foreign Direct Investment (FDI)
in Nigeria using an econometric regression technique of the Ordinary least
square (OLS). The findings of the study also show that trade openness, market
size, infrastructure, exchange rate and inflation rate are statistically significant
in explaining the foreign direct investment in Nigeria while human capital,
labour force and natural resources are statistically insignificant in explaining
the growth of foreign direct investment in Nigeria. The study recommends
that: The government should make polices that will create a business friendly
environment to attract FDI inflows in economy. The government should
provide the needed leadership and also ensure political stability in the
country. This will attract investors to take the advantage of the market size of
the country to FDI into the economy. The government should make policies
that will favour trade openness. Trade openness is found to be factor that
attracts investors invest in the country. This is lesser barriers to trade
encourages investment and the government should provide the needed
infrastructure. Necessary infrastructures that will reduce the cost of doing
business should be the watch word of every government.
KEYWORDS: Foreign Direct Investment (FDI), Trade Openness, Market Size,
Infrastructure, Human Capital, Labour Force, Natural Resources, Exchange Rate
and Inflation Rate
1. INTRODUCTION
Foreign Direct Investment (FDI) has been variously
described by scholar to represent the inflow of investment
from one country to another country that is not that of the
investor. According to The Financial Times (2019), a Foreign
Direct Investment (FDI) is an investment in the form of a
controlling ownership in a business in one country by an
entity based in another country. It is thus distinguished from
a foreign portfolio investment by a notion of direct control. It
is also described as the net inflows of investment to acquire
a lasting management interest (10 percent or more of voting
stock) in an enterprise operating in an economy other than
that of the investor. It is the sum of equity capital,
reinvestment of earnings, other long-term capital, and shortterm capital as shown in the balance of payments. This series
shows net inflows of investment from the reporting economy
to the rest of the world (World Data Atlas, 2017). Foreign
Direct Investment (FDI) is the ownership or control of some
portion of companies or firms by foreigners in a domestic
economy (Oba & Onuoha, 2013). It consist of acquisition or
creation of assets (e.g firm’s equity, buildings, oil drilling
rigs, etc) and in some cases these companies join together
with the government of the domestic economy and termed
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as joint ventures companies (Oba & Onuoha, 2013; Piana,
2005). Research has shown that this form of investment has
contributed enormously to the growth of economies.
According to Dembo and Nyambe (2016), many African
governments lately have been committing stimulus at what
they believe may attract foreign direct investors because of
its contribution to the economy. It was also, argues that each
country has its own attractions. Therefore what may drive
FDI in one region may not drive it in another (Dembo &
Nyambe, 2016; Asiedu, 2002).
In Nigeria, Foreign Direct Investment (FDI) has had a
chequered history by witnessing a period of increase and a
period of sharp decline of inflow of FDI into the economy.
Before the year 1999, FDI inflows into Nigeria have had a
very serious volatility. According to CBN (2006) as cited in
Obida and Abu (2010), FDI inflows increased from N786.40
million in 1980 to N2, 193.40 million in 1982, but soon
dropped to N1, 423.50 million in 1985. The value of FDI rose
from N6, 236.70 million in 1988 to N10, 450.0 million and
N55, 999.30million in 1990 and 1995, respectively.
However, the value of FDI fell drastically to N5, 672.90
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million in 1996 and further to N4, 035.50million in 1999.
According to Obida and Abu (2010), the inflows of FDI has
continued to rise since the year 2001, moving from
N4,937.0million to N13,531.20million in 2003 and
N20,064.40million in 2004. The FDI inflows stood at N41,
734.0million in 2006 (CBN, 2006). However, between the
year 2007 and 2018, there is a serious volatility in FDI
inflows in Nigeria. The amount of FDI inflow from 2007 to
2017 which was recorded in current USD dollars was
6,036,021,405 USD; 8,195,499,253 USD; 8,554,740,717 USD;
6,026,232,041 USD; 8,841,113,287 USD; 7,069,934,205 USD;
5,562,873,606 USD; 4,651,465,948 USD; 3,137,318,700 USD;
4,445,102,771 USD and 3,497,233,435 USD respectively
(World Data Atlas, 2017). The years 2007-2009 recorded a
percentage increase of 24.34 %, 35.78% and 4.38%
respectively in the FDI inflow. Between 2012 and 2015 there
was decline of FDI inflow of -20.03%, -21.32%, -16.38% and
-32.55% respectively. There was a 41.68% increase in 2016
while the year 2017 saw a sharp decline of -21.32% in FDI
inflow. The highest decline was in 2015 which arguably
announced the moving of the country into recession. The
sharp decline in the amount of FDI inflows in to the country
in the year 2015 could be attributed to a number of factors
which arguably was linked to political and leadership factor.
However, a number of macroeconomic variables play
significant role in attracting FDI in an economy.
Consequently, the relevance of foreign direct investment
cannot be overemphasized. Its significant influence on the
provision of new technologies, products, management skills
and competitive business environment, overtime has been a
strong impetus for economic growth. Many countries of the
world, especially emerging economies faviour policies that
encourages the inflow of foreign direct investment because
of it positive spillover associated with the provision of funds
and expertise that could help smaller companies to expand
and increase international sales and transfer of technology
thus, forming new varieties of capital input (i.e. flow of
services available for production from the stock of capital
goods e.g. equipment, structures, inventories etc) that cannot
be achieved through financial investments or trade in goods
and services alone (Asogwa & Manasseh, 2014). Therefore,
investigating factors that propels the inflows of FDI into the
economy is imperative for the obvious economic growth and
development reasons.
1.1. Statement of the Problem
This study was informed by the perceived rising level of job
loss and economic strangulation in the country. It was
reported that between the year 2015 and 2018, the country
recorded over three million job loss thus increasing the
rising level of unemployment in the country (NBS, 2018).
This negative scenario that has also plunged the country into
recession within the period is not unconnected with the huge
amount of FDI that was withdrawn out of the economy
between the year 2015 and 2018 thus suggesting that FDI
plays significant role in the economic growth and
development of any economy. According to Obida and Abu
(2010), Foreign Direct Investment (FDI) not only provides
developing countries (including Nigeria) with the much
needed capital for investment, it also enhances job creation,
managerial skills as well as transfer of technology. All of
these contribute to economic growth and development.
Attracting FDI requires a curious and committed effort in
providing the needed leadership and enabling environment
for such investment as well as policies (fiscal and monetary)
that will attract FDI into the country. This study is therefore
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an attempt to investigated variables that could be
recommended for attracting FDI inflow in Nigeria.
1.2. Objectives of the Study
The main objective of the study is to examine the
determinants of foreign direct investment in Nigeria.
Specifically the study intends to ascertain the influence trade
openness, market size, infrastructure, human capital, labour
force, natural resources, exchange rate and inflation rate on
Foreign Direct Investment (FDI) in Nigeria.
2. EMPIRICAL LITERATURE
Arawomo and Apanisile (2018) investigated the key
determinants of FDI in the Nigerian telecommunication
sector using time series data spanning from 1986 to 2014.
The study modelled annual data on infrastructure,
government expenditure, trade openness and market size,
FDI flow into telecommunication sector, foreign exchange
rate, interest rate and inflation. The analysis was done using
graphs, t-test and Autoregressive Distributed Lag (ARDL).
The results showed that the key determinants of FDI in the
sector are market size and trade openness (t = 5.75 to 9.05; p
< 0.05) on positive side, as well as Inflation and real interest
rate (t = −0.05 to −4.03; p < 0.05) on negative side. Akanegbu
and Chizea (2017) investigated foreign direct investment
and economic growth in Nigeria: An empirical analysis using
regression techniques. The results of the estimation analysis
obtained revealed that there exists a positive relationship
between FDI and output growth in the Nigerian economy.
Gandu and Yusha'u (2017) carried out an analysis of the
impact of foreign direct investment on economic growth in
Nigeria using a quarterly secondary time series data over the
period 2009Q1 to 2016Q4 and autoregressive Distributed Lag
(ARDL) approach to Co-integration and Error Correction
(ARDL-VECM) Model, developed by Pesaran, Shin and Smith
(2001). The results indicate a long-run relationship between
FDI, economic growth, exchange rate, interest rate and
inflation rate. Also, the study further reveals a negative
impact between FDI, exchange rate, interest rate, and
inflation rate on economic growth. Moreover, the coefficient
of error correction model (ECM) suggests that the speed of
adjustment in the estimated model had the expected level
significance and negative sign. However, the Granger
causality test result reveals unidirectional causality
relationship running from FDI inflow to economic growth in
Nigeria. This analysis included inflation rate, interest rate,
exchange rate and FDI as independent variables, while
economic growth as dependent variable. Major findings of
this study included that FDI inflow has significant negative
impact on economic growth in both short run and the long
run. Results demonstrate that FDI, exchange rate, interest
rate and inflation deter economic growth. As such, a major
challenge before the policy managers therefore, is to attain a
stable and realistic exchange rate, lower interest rate and
moderate inflation rate that will encourage foreign investors
to improve the economic growth in Nigeria.
Enisan (2017) examined the determinants of foreign direct
investment in Nigeria using Markov- Regime Switching
Model (MSMs). The paper adopts maximum likelihood
methodology of Markov-Regime Model (MSM) to identify
possible structural changes in level and/or trends and
possible changes in parameters of independent variables
through the transition probabilities. The results show that
FDI process in Nigeria is governed by two different regimes
and a shift from one regime to another regime depends on
transition probabilities. The results show that the main
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determinants of FDI are GDP growth, macro instability,
financial development, exchange rate, inflation and discount
rate. Achugamonu, Ikpefan, Taiwo and Okorie (2016)
examined constraints to foreign direct investment: The
Nigerian experience (1980 - 2015) using Vector Error
Correction Model was used to establish the short run
dynamics and the long run relationship as well as ascertain
the speed of systemic adjustment in the model. The study
found that government external and domestic debts,
inflation rate and exchange rate appreciation (in favour of
the domestic currency) have significant long run relationship
with foreign direct investment in Nigeria. Dembo and
Nyambe (2016) investigated the determinants of foreign
direct investments in Namibia using data covering the period
1984 to 2014. The study employed an Error Correction
regression Model. The short run and long run scenarios were
captured and yielded that in the short run, a depreciation of
the Namibian dollar was found to positively impact on the
receipts of FDI. Inflation and GDP growth were found to
impact positively on FDI in the short and long run scenarios.
Though statistically insignificant, population growth was
found to be a positive driver while exchange rate was
negatively related to FDI in a short-run. An existence of a
long run relationship among the variables was also
confirmed. As for the long run, population growth was
negatively impacting on the attraction of FDI. With the
Namibian dollar pegged to the South African Rand at 1:1,
inflation was seen to have a positive impact on FDI in both
periods. A positive sign for inflation is not necessarily a
doubtful finding in the short-run period, considering that the
opposite of it can be serious on the economy.
Using the ordinary least square multiple regression
statistical technique, Ojong, Arikpo & Ogar (2015) examined
the determinants of Foreign Direct Investment Inflow to
Nigeria with a time series data for the period between 1983
and 2013. Result on the basis of the OLS revealed that there
is a large inverse effect of market capitalization and gross
fixed capital formation on FDI inflow in Nigeria. Also an over
liberal trade policy is a disincentive for foreign direct
investment in Nigeria. Finally, there exists a significant
positive effect of level of economic growth on FDI attraction
in Nigeria. On the basis of the ADF and PP test, all variable
were stationary at first difference. Again, on the basis of the
correlation matrix, all variables were strongly related except
market capitalization, gross fixed capital formation and level
of economic activities which had weak relation with FDI.
Okonkwo, Egbunike and Udeh (2015) investigated foreign
direct investment and economic growth in Nigeria using
annual time series data spanning from the period 1990 to
2012. The study made use of ordinary least squares (OLS)
estimation techniques. The result shows that export assumes
a positive sign which implies that there is a positive
relationship between economic growth and export. Asogwa
and Manasseh (2014) examined the impact of foreign direct
investment on economic growth in Nigeria using quarterly
data covering the period 1980Q1-2009Q4 and econometric
regression model. The empirical evidence shows that FDI
into manufacturing and telecommunication sector has
positive impact on economic growth in Nigeria while FDI
into agricultural sector impacted on economic growth
negatively. The findings on granger causality suggest that
FDI into agriculture, manufacturing and telecommunication
sector have a unidirectional relationship with economic
growth in Nigeria. Institution or legal framework has
positive and significant influence on the inflow of FDI hence
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suggesting the need for strong legal framework for property
right protection could serve as an incentive to attract more
foreign investors. Political instability and real exchange rate
significantly and negatively influences the inflow of FDI visa-vis signifying the importance of friendly business
environment in the country. Onyali and Okafor (2014)
investigated the nexus between foreign direct investment
(FDI) and the vision 2020 economic growth target of Nigeria
using Ordinary Least Square regression technique and
equations. From the findings, it was discovered that
increased inflow of FDI in Nigeria is a major pathway
towards achieving the vision 2020 economic growth target.
Using co-integration and error correction model, Maghori
(2014) investigated the determinants of Foreign Direct
Investment in Nigeria with annual time series data for the
periods 1970 to 2010. The results show that the major
determinant of foreign capital inflow in the economy is the
ratio of external debt to Gross Domestic Product both in the
short run and long run. However, some factors such as the
size of the national income, the degree of openness to trade,
the existing stock of foreign capital in the previous period,
inflation rate and exchange rate are well maintained through
to the long run.
Ndem, Okoronkwo and Nwamuo (2014) examined the
determinants of foreign direct investment and their impacts
on Nigerian Economy. The study investigated how exchange
rate, market size (GDP), investment in infrastructure,
openness and political risks have impacted on the flow of
FDI in Nigeria from 1975 – 2010 using Ordinary Least
Square (OLS), and co-integration Error Correction Method
(ECM) we found out that Market Size (GDP), openness, and
exchange rate impact much on FDI inflow while political risk
was unfavourable to it. Investment in infrastructure was
discovered to be favourable but its level is inadequate to
improve FDI required for sustainable growth and
development. We therefore recommend improvement in
infrastructural development and technological development
through knowledge spill over, maintaining a conducive
political and social environment for development. Oba and
Onuoha (2013) examined factors that influence the foreign
direct investment in Nigeria and their impact on the
economy using data covering the period 2001 -2010 and
considered such variables such as real GDP, inflationary
levels, openness of trade, electricity consumption, transport
and communication. Econometric model and regression
analysis were employed to analyse the data. The results
based on the value of F-statistics (35.83) and the co-efficient
of determination (R2) of 0.98 revealed that the model was
well specified and that the explanatory variables are
sufficient to explain the inflow of FDI to Nigeria. The
negative values of parameters such as the real GDP, inflation
and electricity consumption call for policy reconsiderations.
Ugwuegbe, Okore and Onoh (2013) examined the impact of
foreign direct investment on the Nigerian Economy. The
work covered a period of 1981-2009 using an annual data
from Central Bank of Nigeria statistical bulletin and a growth
model via the Ordinary Least Square method. The result of
the OLS techniques indicates that FDI has a positive and
insignificant impact on the growth of Nigerian economy for
the period under study. Obida and Abu (2010) carried out an
empirical analysis of the determinants of foreign direct
investment in Nigeria using the error correction technique.
The study analyzed the relationship between foreign direct
investment and its determinants. The results reveal that the
market size of the host country, deregulation, political
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instability, and exchange rate depreciation are the main
determinants of foreign direct investment in Nigeria.
In the final analysis, literatures on determinants of foreign
direct investment are rife with robust and insightful findings.
However, there is an asymmetry in the methodology,
variables and area in which the studies are carried out.
Among the studies carried out in Nigeria, none of them
capture the era (2015-2018) in which there was a serious
decrease and withdrawal of FDI in the country and the
factors that propelled the withdrawal. This therefore
warrants an empirical investigation in ascertaining the
determinants of foreign direct investment in Nigeria
covering the period 1999-2018.
3. METHODOLOGY
3.1. Model Specification
This study modeled variables like trade openness, market
size, infrastructure, human capital, labour force, natural
resources, exchange rate and inflation rate as the
determinants of foreign direct investment in Nigeria. Thus,
foreign direct investment will be the dependent variable
while the explanatory variables include trade openness,
market size, infrastructure, human capital, labour force,
natural resources, exchange rate and inflation rate.
Therefore, the model for this study is stated as followed:
The functional form of the model is:
Y = f(X1, X2, X3, X4, X5, X6, X7, X8)
(1)
The mathematical form of the model is specified as:
Y = β0+β1X1+β2X2+β3X3+β4X4+β5X5+β6X6+β7X7+β8X8 (2)
The econometric form of the model is thus:
Y = β0 +β1X1+β2X2+β3X3+β4X4+β5X5+β6X6+β7X7+β8X8+μ (3)
Where
Y = Foreign Direct Investment
X1 = Trade Openness
X2 = Market Size
X3 = Infrastructure
X4 = Human Capital
X5 = Labour Force
X6 = Natural Resources
X7 = Exchange Rate
X8 = Inflation Rate
β0 = Intercept
β1 - β8 = Partial slope coefficients or Parameters of the model
μi = Stochastic error term, which is normally distributed.
The economic technique employed in the study is the
Ordinary Least Square (OLS). This is because the OLS
computational procedure is fairly simple and a best linear
estimator among all unbiased estimation, efficient and
shown to have the smallest (minimum variance) thus, it
become the best linear unbiased estimator (BLUE) in the
classical linear regression (CLR) model. Basic assumptions of
the OLS are related to the forms of the relationship among
the distribution of the random variance (μi). OLS is a very
popular method and in fact, one of the most powerful
methods of regression analysis. It is used exclusively to
estimate the unknown parameters of a linear regression
model. The Economic views (E-views) software was adopted
for regression analysis in this study.
3.2. Stationarity (unit root) test
The importance of this test cannot be overemphasized since
the data to be used in the estimation are time-series data. In
order not to run a spurious regression, it is worthwhile to
carry out a stationary test to make sure that all the variables
are mean reverting that is, they have constant mean,
constant variance and constant covariance. In other words,
that they are stationary. The Augmented Dickey-Fuller (ADF)
test would be used for this analysis since it adjusts for serial
correlation.
Decision rule: If the ADF test statistic is greater than the
MacKinnon critical value at 5% (all in absolute term), the
variable is said to be stationary. Otherwise it is non
stationary.
3.3. Cointegration test
Econometrically speaking, two variables will be cointegrated
if they have a long-term, or equilibrium relationship between
them. Cointegration can be thought of as a pre-test to avoid
spurious regressions situations. As recommended by
Gujarati and Porter (2009), the ADF test statistic will be
employed on the residual.
Decision Rule: if the ADF test statistic is greater than the
critical value at 5%, then the variables are cointegrated
(values are checked in absolute term)
4. PRESENTATION OF EMPIRICAL RESULTS
4.1 Summary of Stationary Unit Root Test
Establishing stationarity is essential because if there is no stationarity, the processing of the data may produce biased result.
The consequences are unreliable interpretation and conclusions. We test for stationarity using Augmented Dickey-Fuller (ADF)
tests on the data. The ADF tests are done on level series, first and second order differenced series. The decision rule is to reject
stationarity if ADF statistics is less than 5% critical value, otherwise, accept stationarity when ADF statistics is greater than 5%
criteria value. The result of regression is shown in table 1 below.
Variables
FDI
TOP
MAS
INFRA
HUM
LAB
NATR
EXCH
INFL
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ADF
Statistics
-6.465304
-6.369529
-9.253889
-4.864043
-6.153296
-4.527896
-6.215947
-5.229408
-5.813439
Table 1: Summary of ADF test results
Lagged
1% Critical
5% Critical
Difference
Value
Value
1
-3.661661
-2.960411
1
-3.661661
-2.960411
1
-3.653730
-2.957110
1
-3.653730
-2.957110
1
-3.661661
-2.960411
1
-3.653730
-2.957110
1
-3.653730
-2.957110
1
-3.653730
-2.957110
1
-3.661661
-2.960411
Source: Researchers computation
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10% Critical
Value
-2.619160
-2.619160
-2.617434
-2.617434
-2.619160
-2.617434
-2.617434
-2.617434
-2.619160
July - August 2019
Order of
Integration
I(1)
I(1)
I(1)
I(1)
I(1)
I(1)
I(1)
I(1)
I(1)
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Evidence from unit root table above shows that none of the variables are stationary at level difference that is, I(0), rather all the
variables are stationary at first difference, that is, I(1). Since the decision rule is to reject stationarity if ADF statistics is less
than 5% critical value, and accept stationarity when ADF statistics is greater than 5% criteria value, the ADF absolute value of
each of these variables is greater than the 5% critical value at their first difference but less than 5% critical value in their level
form. Therefore, they are all stationary at their first difference integration.
4.2 Summary of Cointegration Test
Cointegration means that there is a correlation among the variables. Cointegration test is done on the residual of the model.
Since the unit root test shows that none of the variable is stationary at level I(0) but stationary at first difference 1(1), we go
further to carry out the cointegration test. The essence is to show that although all the variables are stationary, whether the
variables have a long term relationship or equilibrium among them. That is, the variables are cointegrated and will not produce
a spurious regression. The result is summarized in tables 2 below for Trace and Maximum Eigenvalue cointegration rank test
respectively.
Table 2: Summary of Johansen Cointegration Test
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
No. of CE(s)
Trace
Eigenvalue Statistic
0.05
Critical Value
Prob.**
None *
At most 1 *
At most 2 *
At most 3 *
At most 4
At most 5
At most 6
At most 7
At most 8
0.910514
0.820296
0.727523
0.667972
0.577501
0.471838
0.266679
0.210915
0.078934
197.3709
159.5297
125.6154
95.75366
69.81889
47.85613
29.79707
15.49471
3.841466
0.0000
0.0000
0.0019
0.0134
0.0676
0.2029
0.4136
0.2648
0.1048
277.1859
199.9482
145.0219
103.4155
68.13435
40.56413
20.13684
10.21135
2.631169
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized
No. of CE(s)
Max-Eigen
Eigenvalue Statistic
0.05
Critical Value
Prob.**
None *
At most 1 *
At most 2
At most 3
At most 4
At most 5
At most 6
At most 7
At most 8
0.910514
0.820296
0.727523
0.667972
0.577501
0.471838
0.266679
0.210915
0.078934
58.43354
52.36261
46.23142
40.07757
33.87687
27.58434
21.13162
14.26460
3.841466
0.0003
0.0267
0.1441
0.1573
0.2340
0.3123
0.7516
0.4230
0.1048
77.23767
54.92629
41.60643
35.28116
27.57022
20.42729
9.925489
7.580183
2.631169
Source: Researchers computation
Table 2 indicates that trace have only 4 cointegrating variables in the model while Maximum Eigenvalue indicated only 2
cointegrating variables. Both the trace statistics and Eigen value statistics reveal that there is a long run relationship between
the variables. That is, the linear combination of these variables cancels out the stochastic trend in the series. This will prevent
the generation of spurious regression results. Hence, the implication of this result is a long run relationship between foreign
direct investment and other variables used in the model.
Having verified the existence of long-run relationships among the variables in our model, we therefore, subject the model to
ordinary least square (OLS) to generate the coefficients of the parameters of our regression model. The result summary of the
regression test is shown in table 3 below.
Table 3: Summary of regression results
Dependent Variable: FDI
Method: Least Squares
Sample: 1999 2018
Included observations: 21
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Variable
Coefficient Std. Error
t-Statistic
Prob.
C
TOP
MAS
INFRA
225.0446
0.274235
709.4449
23.58039
11.56563
8.026548
5.528819
4.340454
0.0000
0.0090
0.0016
0.0022
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2.974565
1.032979
1.341565
1.759135
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International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
HUM
LAB
NATR
EXCH
INFL
294.1750
25.52904
153.5917
-130.3381
-56.90844
3.778510
2.645770
1.655557
2.112658
4.506256
0.778548
0.964900
0.927734
-3.430297
-3.669019
R-squared
Adjusted R-squared
S.E. of regression
0.769797
0.709868
75.16066
F-statistic
Prob(F-statistic)
Durbin-Watson stat
0.4436
0.3438
0.3624
0.0050
0.0096
63.72141
0.000000
2.435175
Source: Researchers computation
4.3 Discussion of Findings
To discuss the regression results as presented in table 4.3
(see also, appendix 4), we employ economic a priori criteria,
statistical criteria and econometric criteria.
Discussion based on economic a priori criteria
This subsection is concerned with evaluating the regression
results based on a priori (i.e., theoretical) expectations. The
sign and magnitude of each variable coefficient is evaluated
against theoretical expectations. From table 3, it is observed
that the regression line have a positive intercept as
presented by the constant (c) = 225.0446. This means that if
all the variables are held constant or fixed (zero), FDI will be
valued at 225.0446. Thus, the a-priori expectation is that the
intercept could be positive or negative, so it conforms to the
theoretical expectation. It is observed in table 3 that trade
openness, market size, infrastructure, human capital, labour
force and natural resources have a positive impact on foreign
direct investment while exchange rate and inflation rate
have a negative impact on foreign direct investment in
Nigeria, although, exchange rate was expected to be either
positive or negative. This implies that a unit increase in trade
openness, market size, infrastructure, human capital, labour
force and natural resources, will lead to an increase in the
FDI in Nigeria. On the other hand, increases in exchange rate
and inflation rate will lead to a decrease in the FDI.
Discussion based on statistical criteria
This subsection applies the R2, adjusted R2, the S.E and the f–
test to determine the statistical reliability of the estimated
parameters. These tests are performed as follows:
From our regression result, the coefficient of determination
(R2) is given as 0.769797, which shows that the explanatory
power of the variables is very high and/or strong. This
implies that 77% of the variations in the growth of the
foreign direct investment are being accounted for or
explained by the variations in trade openness, market size,
infrastructure, human capital, labour force, natural
resources, exchange rate and inflation rate in Nigeria. While
other determinants of FDI not captured in the model explain
just 23% of the variation in foreign direct investment in
Nigeria. The adjusted R2 supports the claim of the R2 with a
value of 0.709868 indicating that 71% of the total variation
in the dependent variable (economic growth is explained by
the independent variables (the regressors)). Thus, this
supports the statement that the explanatory power of the
variables is very high and strong.
The standard errors as presented in table 3 show that all the
explanatory variables were all low. The low values of the
standard errors in the result show that some level of
confidence can be placed on the estimates. The F-statistic:
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The F-test is applied to check the overall significance of the
model. The F-statistic is instrumental in verifying the overall
significance of an estimated model.
5. CONCLUSION AND RECOMMENDATIONS
The study attempted to explain the determinants of foreign
direct investment in Nigeria from 1999 -2018 using Ordinary
least Square (OLS) technique method. All data used are
secondary data obtained from the Statistical Bulletin of
Central Bank of Nigeria (CBN) and other relevant literature.
In executing the study, the OLS techniques was applied after
determining stationarity of our variables using the ADF
Statistic, as well as the cointegration of variables using the
Johansen approach and was discovered that the variables
are stationary and have a long term relationship among the
variables in the model. From the result of the OLS, it is
observed that trade openness, market size, infrastructure,
human capital, labour force and natural resources have a
positive impact on foreign direct investment while exchange
rate and inflation rate have a negative impact on foreign
direct investment in Nigeria, although, exchange rate was
expected to be either positive or negative. This implies that a
unit increase in trade openness, market size, infrastructure,
human capital, labour force and natural resources, will lead
to an increase in the foreign direct investment in Nigeria. On
the other hand, increases in exchange rate and inflation rate
will lead to a decrease in the foreign direct investment in
Nigeria.
From the regression analysis, the result show that all the
variables conform to the a priori expectation of the study
which indicates that trade openness, market size,
infrastructure, human capital, labour force, natural
resources, exchange rate and inflation rate are good
determinants of foreign direct investment in Nigeria. The Ftest conducted in the study shows that the model has a
goodness of fit and is statistically different from zero. In
other words, there is a significant impact between the
dependent and independent variables in the model.
The findings of the study also show that trade openness,
market size, infrastructure, exchange rate and inflation rate
are statistically significant in explaining the foreign direct
investment in Nigeria while human capital, labour force and
natural resources are statistically insignificant in explaining
the growth of foreign direct investment in Nigeria. Finally,
the study shows that there is a long run relationship exists
among the variables. Both R2 and adjusted R2 show that the
explanatory power of the variables is very high and/or
strong. The standard errors show that all the explanatory
variables were all low. The low values of the standard errors
in the result show that some level of confidence can be
placed on the estimates.
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International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
The most significant factors that determine the performance
of foreign direct investment in Nigeria according to our
findings are trade openness, market size, infrastructure,
exchange rate and inflation rate which were statistically
significant as a result of our decision rule applied. But this is
not undermining the effect of others variables used during
the study period which have in one way or the other affected
the determinants of the performance of FDI. To increase the
inflow of FDI and its performance, the study recommends
that:
1. The government should make polices that will create a
business friendly environment to attract FDI inflows in
economy.
2. The government should provide the needed leadership
and also ensure political stability in the country. This
will attract investors to take the advantage of the market
size of the country to FDI into the economy.
3. The government should make policies that will favour
trade openness. Trade openness is found to be factor
that attracts investors invest in the country. This is
lesser barriers to trade encourages investment.
4. The government should provide the needed
infrastructure. Necessary infrastructures that will
reduce the cost of doing business should be the watch
word of every government.
References
[1] Achugamonu, B. U., Ikpefan, O. A. Taiwo, J. N & Okorie,
U. E. (2016). Constraints to foreign direct investment:
The Nigerian experience (1980 - 2015). 3rd
International Conference on African Development
Issues, 462-467.
[2] Akanegbu, B. N. & Chizea, J. J. (2017). Foreign direct
investment and economic growth in Nigeria: An
empirical analysis. European Journal of Research in
Social Sciences, 5(1), 11-20.
[3] Arawomo, O. & Apanisile, J. F. (2018). Determinants of
foreign direct investment in the Nigerian
telecommunication sector. Modern Economy, 9, 907923.
[4] Asiedu, E. (2002). On the determinants of foreign direct
investment in developing countries: is Africa different?
World development, 30, 107-119.
[5] Asogwa, F. O. & Manasseh, C. O. (2014). The impact of
foreign direct investment on economic growth in
Nigeria. Journal of Economics and Finance, 3(5), 37-45.
[6] Dembo, J. S. & Nyambe, J. M. (2016). Investigating the
determinants of foreign direct investments in Namibia.
European Journal of Business, Economics and
Accountancy, 4(5), 93-104.
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|
Unique Paper ID – IJTSRD25293
|
[7] Enisan, A. A. (2017). Determinants of foreign direct
investment in Nigeria: A markov regime-switching
approach. Review of innovation and competitiveness,
3(1), 21 – 48.
[8] Gandu, S. & Yusha'u, I. A. (2017). Analysis of the impact
of foreign direct investment on economic growth in
Nigeria. International Journal of Operational Research
in Management, Social Sciences & Education, 3(2), 88100.
[9] Gujarati, D. N. & Porter, D. C (2009). Basic
Econometrics. New York. McGraw Hill International
Edition.
[10] Maghori, E. (2014). Determinants of foreign direct
investment in Nigeria “Evidence from co-integration
and error correction modeling”. International Journal of
Business and Social Science, 5(6), 215-224.
[11] Ndem, S. E., Okoronkwo, O. & Nwamuo, C. (2014). The
determinants of foreign direct investment and their
impacts on Nigerian economy (1975 – 2010). Journal of
Integrative Humanism, March,74-84.
[12] Oba, U. O. & Onuoha, B. C. (2013). The determinants of
foreign direct investments (FDI) and the Nigerian
Economy. American International Journal of
Contemporary Research, 3(11), 165-172.
[13] Obida, G. W. & Abu, N. (2010). Determinants of foreign
direct investment in Nigeria: An empirical analysis.
Global Journal of Human Social Science, 10(1), 26-34.
[14] Ojong, C. M., Arikpo, O. F. & Ogar, A. (2015).
Determinants of foreign direct investment inflow to
Nigeria. Journal of Humanities and Social Science, 20(8),
34-43.
[15] Okonkwo, R. I., Egbunike, F.C. & Udeh, F. N. P. (2015).
Foreign direct investment and economic growth in
Nigeria. Developing Country Studies, 5(9), 47-52.
[16] Onyali, C. I. & Okafor, T. (2014). Foreign direct
investment and the Nigerian economy: Vision 2020
mission. International Journal of Business and Finance
Management Research, 2(2014), 8-16.
[17] Piana, V. (2005) Foreign Direct Investment. Economics
WEB
instituted.
Retrieved
from
http://www.economicwebinstituted.org
[18] Ugwuegbe, Okore & Onoh (2013). The impact of
foreign direct investment on the Nigerian economy.
European Journal of Business and Management, 5(2),
25-33.
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